Prediction of metastatic and recurrence in prostate cancer patients based on BMP-related differential gene indicators in prostate cancer bone metastases

Objectives: Bone metastasis is the leading cause of death in patients with advanced prostate cancer (PCa). The present study aimed to construct a prognostic risk model based on bioinformatics methods that can predict bone metastasis in prostate cancer, and investigated the effect of risk scores on prognosis and immune infiltration. Methods: Differentially expressed genes (DEGs) were identified in bone metastases and primary PCa samples and intersected with BMP-related genes from the MisigDB database. Then LASSO regression analysis was used to establish the prognostic risk model. Independent prognostic factors for PCa bone metastases were identified by multivariate Cox regression analysis and a prognostic nomogram was created. The patients were further divided into high and low-risk groups according to median risk score, so as to compare functional enrichment analysis and immune cell infiltration between the groups. Results: After the intersection of DEGs from PCa bone metastasis samples and BMP-related genes, four genes were screened by Lasso regression to construct a prognostic risk model. Multifactorial regression analysis identified risk score as an independent prognostic factor for PCa bone metastasis, and Kaplan–Meier curve showed that patients in the high-risk group were more likely to develop bone metastasis and biochemical recurrence(BCR). The risk score was also positively correlated with some prognostic related clinicopathological features. The DEGs of high-risk group were significantly enriched in the proliferation of epithelial cells and extracellular matrix bioprocesses by GO analysis, and also enriched in mesenchymal transition, inflammatory response, angiogenesis, and multiple tumor metastasis gene sets by GSEA analysis. In addition, the high-risk group possessed a higher degree of immune cell infiltration, CAFs, macrophages, Estimate scores, and stromal scores than the low-risk group. Conclusion: The prognostic risk model of BMP-related genes constructed based on the GEO database can effectively predict the occurrence of PCa bone metastasis, and the high-risk score is closely related to the prognosis and immune infiltration of PCa patients.